1. Music and Audio Information Search System
Kiavash Bahreini –

2. Outline Introduction Semantic Web What is Ontology? Domains and ranges
Ontology Languages (OWL)
Search on the Web
Object-oriented Modeling Paradigm
Adaptable Inference Capabilities
Background
The Music Ontology and OWL
Protégé
JBuilder 2006
Jena 2.3 Ontology API

3. Outline (cont) RDQL Java Server Pages Algernon Tomcat
Microsoft SQL Server
Music and Audio system use case, implementation and execution
Execution of program in Browser
Running queries in Algernon
Running queries in SQL Server 2005
Some source codes for classes in Music and Audio ontology
Comparison of Semantic Search and Regular Search
Conclusion
References

4. Introduction
Internet changed the music industry. At first, sharing systems like Napster allowed people to share any song they had on their computer with millions other people.
Communities like this ontology started to appear.
The Music Ontology is an attempt to link all the information about musical Artists, Albums and Tracks together: from MusicBrainz to my ontology.

5. Introduction (Cont)
The goal is to express all relations between musical information to help people finding anything about music and musicians. It is based around the use of machine readable information provided by any web site or web service on the Web.

6. Semantic Web Common framework Allows data Across domains
Sharing
Reuse
Across domains
Application
Enterprise
Community boundaries
Based on Resource Description Framework (RDF)
XML for syntax
URIs for naming.

7. What is Ontology?
At the heart of all Semantic Web applications is the use of ontologies. A commonly agreed definition of an ontology is: ‘An ontology is an explicit and formal specification of a conceptualisation of a domain of interest.

8. Domains and ranges

9. Ontology Languages (OWL)
OWL Lite
OWL DL
OWL Full

10. Search on the Web
Seeking information on the Web is widely used and will become more important as the Web grows. Nowadays, search engines browse through the Web seeking given terms within web pages or text documents without using ontologies.
Traditional search engines such as Yahoo are based on full-text search. These search engines are seeking documents, which contain certain terms.

11. Object-oriented Modeling Paradigm
In the past decade the object-oriented paradigm has become prevalent for conceptual modeling.
Object-oriented models can easily be visualized, thus making understanding conceptual models much simpler. Hence, any successful ontology modeling approach should follow the object-oriented modeling paradigm.

12. Adaptable Inference Capabilities
Inference mechanisms for deduction of information not explicitly asserted is an important characteristic of ontology-based systems. However, systems with very general inference capabilities often do not take into account other needs, such as scalability and concurrency.
For example, in the RDFS and OWL ontology languages it is possible to make some classes the domain or the range of some property. This statement can be interpreted as an axiom saying that for any property instance in the ontology, the source and target instances can be inferred to be members of the domain and target concepts, respectively.

13. Background
The Music Ontology is an effort of ZitGist LLC. to express musical relationships between artists, albums and tracks.
I used OWL and to query that same information using the RDQL query language for RDF and OWL.
The Music Ontology is mainly influenced by the MusicBrainz community music metadatabase. Most of the properties of this ontology reflect the relationships described in that database. Most of the relationship descriptions written in this document have been taken on the MusicBrainz Wiki

14. The Music Ontology and OWL
The Music Ontology is an application of the Ontology Web Language (OWL) because the subject area I am describing – music: albums, artists, audio files, audio file formats, encoding audio files, genre, instrument, key, note, official, resource, rhythm etc has so many competing requirements.
By using OWL, the Music Ontology gains a powerful extensibility mechanism, allowing Music-Ontology-based descriptions to be mixed with claims made in any other OWL and RDF vocabulary

15. Protégé Protégé is Developed by Stanford University, USA
an ontology editor
knowledge-base editor
an open-source, Java tool
provides extensible architecture to create customized knowledge-based applications.
Developed by Stanford University, USA

16. Music and Audio Ontology (Classes)
Provides information on
Album
Artist
AudioFile
AudioFileType
Encoding
Instrument
Key
Live
Note
Official
Resource
Rhythm
Signal
Soundtrack
Spokenword
Track
Type

17. Music and Audio Ontology (Data Type and Object Properties)
Provides information on
arranged
covered
djmix_of
duration
image
linkto_wikipedia
medley_of
member_of
performed
similar_to
trackNum
translation_of

18. Music and Audio Ontology (General structure)

21. JBuilder 2006
The system is written in JBuilder JBuilder is and IDE (Integrated Development Tools) for developing new application, web etc software based on Java Language. All of the packages and classes for using OWL and running queries are imported into this IDE.

22. Jena 2.3 Ontology API
Jena 2.3 Ontology API is a Java framework for building Semantic Web applications. Use RDF models in your Java applications with the Jena Semantic Web Framework.

23. RDQL
RDQL is a query language for RDF in Jena models. The idea is to provide a data-oriented query model so that there is a more declarative approach to complement the fine-grained, procedural Jena API.
It is "data-oriented" in that it only queries the information held in the models; there is no inference being done. Of course, the Jena model may be 'smart' in that it provides the impression that certain triples exist by creating them on-demand.

24. Java Server Pages
JavaServer Pages (JSP) technology allows web developers and designers to rapidly develop and easily maintain information-rich, dynamic web pages that leverage existing business systems. As part of the Java family, the JSP technology enables rapid development of web-based applications that are platform independent.
In theory, JavaServer Pages technology separates the user interface from content generation, enabling designers to change the overall page layout without altering the underlying dynamic content.

25. Algernon
Algernon is a rule-based inference system, implemented in Java and interfaced with Protégé and it is developed by Micheal Hewett. It allows executing queries within the Protégé GUI.
It performs forward and backward rule-based processing of knowledge bases, and efficiently stores and retrieves information in ontologies and knowledge bases. It has an ability to call external Java methods and an internal LISP subsystem.

26. Tomcat
Tomcat is the official reference implementation of the Java Servlet 2.2 and JavaServer Pages 1.1 technologies. Developed under the Apache license in an open and participatory environment, it is intended to be a collaboration of the best-of-breed developers from around the world.
Tomcat is a servlet container and JavaServer Pages(tm) implementation. It may be used stand alone, or in conjunction with several popular web servers:
Apache, version 1.3 or later
Microsoft Internet Information Server, version 4.0 or later
Microsoft Personal Web Server, version 4.0 or later
Netscape Enterprise Server, version 3.0 or later

27. Microsoft SQL Server
Microsoft SQL Server 2005 is a database and data analysis platform for large-scale online transaction processing (OLTP), data warehousing, and e-commerce applications.
The Database Engine is the core service for storing, processing, and securing data. The Database Engine provides controlled access and rapid transaction processing to meet the requirements of the most demanding data consuming applications within your enterprise. The Database Engine also provides rich support for sustaining high availability.

28. Music and Audio system use case, implementation and execution

29. Music and Audio system use case, implementation and execution
Execution of program in Browser
JBuilder 2006
Protege 3.2
OWL File
Ontology & Knowledge Base
User interface
JSP
Pages
Computation Engine
Jena 2.3 OWL API
Inference Engine
RDQL
Serarch Query

30. Music and Audio system use case, implementation and execution
Execution of program in Browser

31. Music and Audio system use case, implementation and execution
Running queries in Algernon
1) Find all artist names that their age=45, nationality="Turkish", country "Turkey", and numberOfAlbums=50:
((:BIND ?age 45)
(:BIND ?nationality "Turkish")
(:BIND ?country "Turkey")
(:BIND ?numberOfAlbums 50)
(:INSTANCE Artist ?aName)
(age ?aName ?age)
(nationality ?aName ?nationality)
(country ?aName ?country)
(numberOfAlbums ?aName ?numberOfAlbums)
(:FAIL (:neq ?age ?age))
(:FAIL (:neq ?nationality ?nationality))
(:FAIL (:neq ?country ?country))
(:FAIL (:neq ?numberOfAlbums ?numberOfAlbums)))

32. Music and Audio system use case, implementation and execution
Running queries in Algernon
2) Find all WebSites Address for artists:
((:BIND ?hasResource "N/A")
(:INSTANCE Resource ?AtristWebsiteAddress)
(hasResource ?AtristWebsiteAddress ?hasResource)
(:FAIL (:neq ?hasResource ?myLink)))

33. Music and Audio system use case, implementation and execution
Running queries in Algernon
3) Find all album names which are not linkto_wikipedia site:
((:BIND ?linkto_wikipedia "N/A")
(:INSTANCE Album ?albumName)
(linkto_wikipedia ?albumName ?linkto_wikipedia)
(:FAIL (:neq ?linkto_wikipedia ?linkto_wikipedia)))

34. Music and Audio system use case, implementation and execution
Running queries in SQL Server 2005
1) Find all artist names that their age=45, nationality="Turkish", country "Turkey", and numberOfAlbums=50:
SELECT DISTINCT [name], [age], [nationality],
[country], [numberOfAlbums]
FROM dbo.tblArtist
WHERE [age]=45
AND [nationality]='Turkish'
AND [country]='Turkey'
AND [numberOfAlbums]=50 GO

35. Music and Audio system use case, implementation and execution
Running queries in SQL Server 2005
2) Find all WebSites Address for artists:
SELECT [id], [name], [hasResource]
FROM dbo.tblResource
WHERE [hasResource]='N/A' GO

36. Music and Audio system use case, implementation and execution
Running queries in Algernon
3) Find all album names which are not linkto_wikipedia site:
SELECT [id], [name], [linkto_wikipedia]
FROM dbo.tblAlbum
WHERE [linkto_wikipedia]='N/A' GO

37. Music and Audio system use case, implementation and execution
Some source codes for classes in Music and Audio ontology:
<owl:Class rdf:ID="Album">
<rdfs:comment rdf:datatype=" g" >Album - This is a generic term defining a package of tracks. This applies to compact disks, vinyls, CD singles, EPs, etc.
</rdfs:comment>
<rdfs:subClassOf>
<owl:Class rdf:about="#MyClasses"/>
</rdfs:subClassOf>
<owl:Restriction>
<owl:maxCardinality rdf:datatype=" >1</owl:maxCardinality>
<owl:onProperty>
<owl:DatatypeProperty rdf:ID="albumName"/>
</owl:onProperty>
</owl:Restriction>
</owl:Class>

38. Comparison of Semantic Search and Regular Search
In relational database management systems (RDBMS), there is no class relationship. It means sub classes cannot inherit all properties from their super classes and also there is no instantiation.
Extra work will be required to define class relations and all properties separately.
In semantic databases there are domains and ranges which can apply for each property but in an RDBMS it is impossible.
There is no object property for these systems.
In relational database Vastly coding implementation for returning data is need.
There is no way for using and importing ontologies, and for returning objects data types.

39. Comparison of Semantic Search and Regular Search
By using OWL, the Music Ontology gains a powerful extensibility mechanism, allowing Music-Ontology-based descriptions to be mixed with claims made in any other OWL or RDF vocabularies. I can re-use other ontologies to describe different relationship between classes.
OWL provides the Music Ontology with a way to mix together different descriptive vocabularies in a consistent way. Vocabularies can be created by different communities and groups as appropriate and mixed together as required, without needing any centralized agreement.
On the other hand, many existing SW tools are still file-oriented and also mine. This limits the size of ontologies that can be processed, as the whole ontology must be read into main memory. Further, the multi-user support and transactions are typically not present, so the whole infrastructure realizing these requirements must be created from scratch but my project is web based and it is able support multi-user transactions.

40. Conclusion
The Music Ontology is an application of the Ontology Web Language (OWL) because the subject area are – music: artists, albums and tracks etc -- has so many competing requirements that a standalone format would not capture them or would lead to trying to describe these requirements in a number of incompatible formats. By using OWL, the Music Ontology gains a powerful extensibility mechanism, allowing Music-Ontology-based descriptions to be mixed with claims made in any other OWL vocabulary.
OWL provides the Music Ontology with a way to mix together different descriptive vocabularies in a consistent way. Vocabularies can be created by different communities and groups as appropriate and mixed together as required, without needing any centralized agreement.
In summary then, OWL is self-documenting in ways which enable the creation and combination of vocabularies in a devolved manner. This is particularly important for an ontology which describes communities, since online communities connect into many other domains of interest, which it would be impossible (as well as suboptimal) for a single group to describe adequately in non-geological time.

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42. References (Cont1)
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43. References (Cont2)
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[35]- Microsoft SQL Server 2005 Documentation

44. Thank you
Any Questions?